Abnormal motion detector and monitor

ABSTRACT

In an embodiment, a seizure monitor provides intelligent epilepsy seizure detection, monitoring, and alerting for epilepsy patients or people with seizures. In an embodiment, the seizure monitor may be a wearable, non-intrusive, passive monitoring device that does not require any insertion or ingestion into the human body. In an embodiment, the seizure monitor may include several output options for outputting the accelerometer/gyro or other motion sensor data and video data, so that the data may be immediately validated and/or remotely viewed. The device alerts are communicated to the outside care givers via wireless or wired medium. The device may also support recording of accelerometer or other motion sensor data and video data, which can be reviewed later for further analysis and/or diagnosis. The device and invention is also used and applicable for other body motion disorders or detection and diagnostics.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.14/599,277, filed Jan. 16, 2015, currently pending, which is adivisional of U.S. application Ser. No. 14/140,424, filed Dec. 24, 2013,now abandoned, which is a continuation of U.S. application Ser. No.13/317,676, filed Oct. 25, 2011, now abandoned, which is a divisional ofU.S. application Ser. No. 12/154,085, filed on May 19, 2008, and whichissued on Dec. 13, 2011 as U.S. Pat. No. 8,075,499, and which claims thebenefit of U.S. Provisional Application No. 60/930,766, filed on May 18,2007. Each of these applications are incorporated herein by reference.

FIELD

This specification is related to medical devices.

BACKGROUND

The subject matter discussed in the background section should not beassumed to be prior art merely as a result of its mention in thebackground section. Similarly, a problem mentioned in the backgroundsection or associated with the subject matter of the background sectionshould not be assumed to have been previously recognized in the priorart. The subject matter in the background section merely representsdifferent approaches, which in and of themselves may also be inventions.

Over two million people (about 1-3% of the population) suffer fromepileptic seizures in the United States. During a seizure the patient isunable to get help, talk, think, or act. In many cases it is veryimportant for doctors and caregivers to be able to detect seizures andgive the patient immediate help. There are some types of seizures, ifnot attended to, that can be fatal. Currently there are no home orpersonal seizure monitoring or detecting devices. There areElectroencephalography (EEG) machines, which measure electrical activityin the brain. However, EEGs are for hospital use and are large andexpensive. The EEGs may analyze brainwaves to detect the onset or theoccurrence of a seizure. EEGs require probes to be mounted on thepatients' scalp to sense, extract, and transmit data. The probes areuncomfortable, intrusive, and awkward—restricting patients' movementsand causing scarring. In addition, the graphs from the EEGs need to bereviewed and interpreted manually by trained personnel, such as nursesand medical assistants.

BRIEF DESCRIPTION

In the following drawings like reference numbers are used to refer tolike elements. Although the following figures depict various examples ofthe invention, the invention is not limited to the examples depicted inthe figures.

FIG. 1A shows a block diagram of an embodiment of seizure detectionsystem 100.

FIG. 1B shows a motion detector embodiment.

FIG. 2A shows a block diagram of system 200, which may be incorporatedwithin the system of FIG. 1.

FIG. 2B shows a block diagram of an embodiment of memory system 206.

FIG. 3A shows a block diagram of an embodiment of motion detector 110.

FIG. 3B shows a block diagram of camera 360.

FIG. 4 is a flowchart of an embodiment of a method 400 of detecting aseizure, based on optical flow.

FIG. 5 is a flowchart of an embodiment of a method 500 of detecting aseizure, based on motion analysis.

FIG. 6 is a flowchart of an embodiment of a method 600 of detecting aseizure, based on patterns of motion vector patterns.

FIG. 7 is a flowchart of an embodiment of a method 700 of detectingseizures by measuring motion, via accelerometers and/or gyro sensors.

FIG. 8 shows a graph 800 of three orthogonal components of accelerationof an arm.

FIG. 9 shows a graph 900 of two parameters of motion.

FIG. 10 shows an embodiment of a seizure detection, analyzing, andmonitoring device.

DETAILED DESCRIPTION

Although various embodiments of the invention may have been motivated byvarious deficiencies with the prior art, which may be discussed oralluded to in one or more places in the specification, the embodimentsof the invention do not necessarily address any of these deficiencies.In other words, different embodiments of the invention may addressdifferent deficiencies that may be discussed in the specification. Someembodiments may only partially address some deficiencies or just onedeficiency that may be discussed in the specification, and someembodiments may not address any of these deficiencies.

In general, at the beginning of the discussion of each of FIGS. 1-3 is abrief description of each element, which may have no more than the nameof each of the elements in the one of FIGS. 1-3 that is being discussed.FIGS. 1-3 refer to FIGS. 1A, 1B, 2A, 2B, 3A, and 3B wherein thedescription may interchangeably recite a singular (e.g., FIG. 1) orplural (e.g., FIGS. 1A, 1B) designation. After the brief description ofeach element, each element is further discussed in numerical order. Ingeneral, each of FIGS. 1-7 is discussed in numerical order and theelements within FIGS. 1-7 are also usually discussed in numerical orderto facilitate easily locating the discussion of a particular element.Nonetheless, there is no one location where all of the information ofany element of FIGS. 1-7 is necessarily located. Unique informationabout any particular element or any other aspect of any of FIGS. 1-7 maybe found in, or implied by, any part of the specification.

In an embodiment, a seizure monitor provides intelligent epilepsyseizure detection, monitoring, and alerting for epilepsy patients and/orother people that experience seizures. In an embodiment, the seizuremonitor is a small consumer-usable device that is wearable and can beset up and used easily by patients. In an embodiment, the seizuremonitor is compact and low cost. The seizure monitor may have at leastany one of the following three different configurations or embodimentsusing—(i) motion sensor data (such as data from accelerometers,gyroscope sensors and/or other motion sensor data), or (ii) video data,and/or (iii) hybrid data (which may include both video and accelerometeror other motion sensor data). In an embodiment, the seizure monitor maybe a wearable, non-intrusive, passive monitoring device that does notrequire any insertion or ingestion into the human body. In anembodiment, the seizure monitor may include several flexible and easyoutput options for outputting motion data, so that the data may beimmediately validated and/or remotely viewed. The seizure monitor mayalso support recording of motion data that can be reviewed later by amedical professional for further analysis and/or diagnosis.

Seizure Detection System (FIGS. 1-3B)

FIG. 1A shows a block diagram of an embodiment of seizure detectionsystem 100. Seizure detection system 100 may include cameras 102 a-n,communications line 104, surfaces 106 a-m, patient 108, motion detector110, and receiver 112. In other embodiments, seizure detection system100 may include additional components and/or may not include all of thecomponents listed above.

Seizure detection system 100 may detect, monitor, and/or alert aconcerned party of the onset and occurrence of epileptic seizures inpatients. In this specification the term “concerned party” includes anyentity or person that may have an interest in knowing about theoccurrence of a seizure, such as caregiver, medical professional, closefriend, or relative of the patient. In an embodiment, seizure detectionsystem 100 may be passive, compact, and/or non-intrusive.

Although throughout the specification seizure detection system 100 isdiscussed, seizure detection is just one example of a motion disorderthat may be detected with seizure detection system 100. Although thediscussion of this specification focuses on seizure monitoring anddetection, there are other motion based diagnostics or body motionanalyses that may be performed using the same system.

Each analysis can be different. But the HW can use the same. Just therules or conditions or pattern recognition can be different.

Cameras 102 a-n may include any number of cameras, which film a patientin order to capture on film images that may be analyzed to determinewhether a seizure is in progress. As also elaborated upon elsewhere,cameras 102 a-n are optional. Cameras 102 a-n are optional, and may bereplaced with another form of detecting seizures, such as motiondetectors. For example, another type of motion detector, such asinfrared detectors, may be used instead of, or in addition to, cameras102 a-n. Although cameras 102 a-n are illustrated as being mounted onsurfaces within a premise of the patient, cameras 102 a-n may be mountedon the patient, and instead of observing the patient to determine themotion of the patient, the motion of the patient may be inferred fromthe images of the surroundings of the patient, for example.

Optical sensors, such as video camera 102 a-n, can be used to monitorthe patient and detect seizure-like activities. Seizure-like activitiesmay offer unique motion patterns and can be distinguished fromnon-seizure-like activities. Easily distinguishable feature points (orfeature points corresponding to particular body parts that aresignificant in determining whether or not a seizure is in progress) inthe scene can be computed (such as those on the person or an object) andthe temporal motion patterns of the feature points (such as points onthe person's body or clothes) can be analyzed across frames fordetection of any abnormal activity. Some motion patterns of interestcould be large movements in short periods of time, repetitive movements,back-and-forth movements, etc. Further, some special seizure activitiesoffer very characteristic and predictable motion patterns, a priorknowledge of which can be utilized effectively to detect such cases.

Communications line 104 communicatively connects cameras 102 a-n to aprocessor (for analyzing the motion data and determining whether aseizure is occurring) and/or seizure alert system. In an embodiment,instead of or in addition to communications line 104, cameras 102 a-nmay communicate wirelessly with a processor and/or seizure alert system.

Surfaces 106 a-m support cameras 102 a-n. Patient 108 suffers fromseizure and is monitored by the rest of seizure detection system 100 todetermine whether a seizure has occurred. In this specification the word“patient” refers to any individual that is being monitored to determinewhether a seizure is occurring.

Motion detector 110 is mounted on patient 108 so that motions of thepatient may be measured. In an embodiment, motion detector 110 is anaccelerometer, a gyro sensor, and/or a hybrid of both. Motion detector110 communicates wirelessly (or via wires) with a seizure alert system.Motion detector 110 may include a transmitter for transmittinginformation about motion measured by motion detector 110. Optionally,motion detector 110 may include a location determining unit (e.g., aglobal positioning unit) for determining the location of patient 108 andtransmitting the location of the patient 108 to a concerned party. In anembodiment there are multiple motion detectors 110 mounted on patient108. In another embodiment, there is only one motion detector 110mounted on patient 108. If motion detector 110 is a small accelerometer(and/or gyro sensor), no other element needs to be worn or placed onpatient 108, and the accelerometer (and/or gyro sensor) may be passiveand non-intrusive. Although often in this specification an accelerometerand/or gyro are referred to, another motion sensor(s) may be usedinstead. The detection of the seizure or movement patterns can be doneinside the watch itself. The results and/or an alert may be sent outsidevia wireless or wired medium. Alternatively, the watch can simply sendonly the sensor data, and the detection and decision can be made outsideon the phone or a computer outside the watch sensor. Both options areviable.

One way of detecting seizures is to monitor the motion of one or moreparts of the body of patient 108. During a seizure there are rapid andjerky movements of one or more body parts, such as the hands, legs,torso, and head. Seizures can be detected by measuring the change inoutput of a motion detector, the frequency of the change, and/oramplitude of the change indicating a movement of one or more body parts.

There are different types of motion detectors, which may be used formotion detector 110. One type of motion detector is an accelerometer,which measures acceleration. Ordinarily, when stationary, each part ofthe body experiences the acceleration of gravity (an accelerometercannot tell the difference between a body being accelerated as a resultof the body's changes in velocity and the body being pulled by a force,such as gravity). From the changes in acceleration, changes in positionand/or velocity may be inferred. When a body part moves, theacceleration of the body part changes, and thus the change in theacceleration indicates a motion. Another type of sensor data is gyrosensor data. Gyro sensors may detect the rotation along X, Y, and/or Zaxes. The rotation angles and position can also be used to detect motionand particular types of motion. While accelerometer measures linear axischanges, gyros measure the rotation changes.

The sensor that is used as motion detector 110 may be a small devicethat can fit into an enclosure the size of a wristwatch. The motion datamay be measured in two dimensions (e.g., along two perpendicular axes,which may be referred to as the X and Y axes) and/or three dimensions(e.g., along three perpendicular axes, which may be referred to as theX, Y, and Z axes). Acceleration, frequency, and amplitude (angle,angular velocity, angular acceleration, and angular impulse or jerk)values above a certain threshold are indicative of abnormal bodymovements that occur during a seizure. If two two-dimensional (X,Y)motion sensors are used as motion detector 110, each of thetwo-dimensional (X,Y) motion sensors may be paced along perpendicularaxes. The two-dimensional and/or three-dimensional motion sensors can beuseful to detect the jerky and back and forth movements. The detectionalgorithm is discussed below.

Motion detector 110 can be positioned and/or mounted on the patient'sarms, legs, bedclothes, and/or the bed itself Motion detector 110 placedon the patient's body may be more effective and accurate in detectingseizures.

Seizure alert system 112 alerts a concerned party when a seizure occurs.Seizure alert system 112 may be a PC, laptop, PDA, mobile phone bell,and/or other unit capable of indicating an alert. Information in signalsfrom cameras 102 a-n and/or motion detector 110 are analyzed, and if itis determined that a seizure is occurring, an alert is output from alertsystem 112. Seizure alert system 112 may include a monitor fordisplaying seizure alerts and/or for displaying motion data. In anembodiment, seizure alert system 112 may include a processor foranalyzing the signals from cameras 102 a-n and/or motion detector 110.In an embodiment, system 100 is a general purpose alerting system andcan also alert other motion disorders.

Motion sensors that are included within motion detector 110 may beattached to the wrist to explicitly capture the motion along the x, y,and z directions. The data obtained may be a time sequence of theinstantaneous acceleration experienced by the sensor. Several approachescan be used here to detect any abnormal seizure-like activities. Theseapproaches can be divided broadly into two categories:

In an embodiment, a processor, which may be located in seizure alertsystem 112 or elsewhere may run algorithms to determine whether aseizure is occurring, and when it is determined that a seizure isoccurring alerts may be output from seizure alert system 112. In anembodiment, along with the alerts, one or more confirmation imagesand/or accelerometer or other motion sensor plots may also be sent to aconcerned party, such as a doctor and/or other caregiver. The alert maybe sent, via SMS, MMS, email, IM, or WAP or other message protocol. Thealert may include an alert message, alarm signal, beeps, local in-devicesound alerts, and/or alarms, which may be produced by a PDA, mobilephone, or other device, which may have built in alarms and alerts.

Receiver 114 receives signals from transmitters on wireless units suchas motion detectors 110. Receiver 114 is optional. If cameras 102 a-n donot communicate via communications line 104 (e.g., if communicationsline 104 is not present), receiver 114 may receive signals from cameras102 a-n. Thus, depending on the embodiment, seizure detection system 100may detect motion via video data from cameras 102 a-n, sensor data frommotion detector 110, and/or hybrid data (which is data based on bothcameras 102 a-n and/or sensor data from motion detector 110).

One example of an embodiment encompassed within FIG. 1 may include inputsensors (video and/or motion sensors, such as accelerometers), one ormore computers for receiving and processing data from the sensors,connectivity interfaces, and a system for remotely monitoring and foralerting (e.g., sending an alarm) a concerned party. The connectivityinterface may include any of a number of communications interfaces, suchas Bluetooth interfaces and/or Wifi, wireless interfaces, and/or wiredinterfaces, which may use IP/LAN connections and/or serial portconnections, such as USB.

Another example of an embodiment encompassed within FIG. 1 may be avideo based system, which may include one or more cameras (such asanalog cameras, WebCam cameras, and/or IP/Network cameras) and/or IRdetectors and/or illuminators for nighttime monitoring and analysis. Thecameras may be color or infrared cameras, for example. The cameras canbe connected to a Personal Computer (PC) and/or any computing device,via (i) a wireless interface, such as a Wifi/LAN interface, a Serial/USBwireless interface, and/or BlueTooth interface, and/or (ii) a wiredinterface such as a LAN and/or Ethernet interface. Video data isreceived by the computing device analyzed and/or processed. The resultsof the analysis and/or processing are transmitted to the concernedparty. Alternatively, processing intelligence, an analysis engine,and/or algorithms that reside in the computing device may be embeddedand/or otherwise built into the camera resulting in a “Smart Camera.” Inan embodiment, there may be a camera and optional IR illuminators fornighttime monitoring and analysis, and there may not be anaccelerometer. There may be a processor that executes the algorithm, andthe processor may be located inside or outside of the camera. Detectionas a result of analyzing the motion data may occur within the processorin the camera or in a processor located elsewhere. Alerts may becommunicated via an external media to a concerned party.

In a video based embodiment, the input data to the processor may be astream of video data from the camera. The camera may be connected to aPC or other computing device, such as a PDA or smart mobile phone. Thecamera may also have an intelligent processor embedded inside, thatprocesses and analyzes the input data.

In a film based seizure detection system, alerts and other output maycommunicate via alarms, SMS, MMS, IM, WAP message, email, or a phonecall to the concerned party. Alerts may be sent either over a LAN (awired network), a wireless network, or over a GSM/cellular mobilenetwork.

Another example of an embodiment encompassed within FIG. 1 may be anaccelerometer (and/or a gyro sensor) based system, which may include anaccelerometer (as a motion sensor). The accelerometers may providetwo-dimensional and/or three-dimensional data related to one or moreaxes of acceleration, rotation, and/or velocity, if available. Theaccelerometer may be communicatively connected to the rest of seizuredetection system 100 by a Wired/LAN interface,Wireless/BlueTooth/ZigBee/Wifi interface, and/or Serial/USB interface.The processor that processes the motion data may be located within anexternal PC or other device, such as a smart mobile phone or otherhandheld device.

In an embodiment, there may be a motion sensor (an accelerometer or gyrosensor) without any video camera, which is simpler and cheaper. Theremay be a processor that operates the algorithm, and the processor may belocated inside or outside of the motion sensor. In other words,detection may be performed via an algorithm executing within theprocessor to determine if a particular motion is a seizure. Alerts maybe communicated from the processor via external any of a number of mediato a concerned party.

In the embodiment that is based on a motion sensor, the input data maybe a stream of analog or digital signals or values from theaccelerometer/gyro sensor. The accelerometer or other motion sensor mayhave a built in processor to interpret and process the input data.Alternatively the accelerometer or other motion sensor may be connectedto a PC or other computing device, such as a smart mobile phone or PDA.The processor may execute seizure detection algorithms.

In the embodiment, that in which an accelerometer is the motion detector110, alerts and other output may be communicated via alarms, SMS, WAPmessages, email, or phone calls to the people specified. Alerts may besent either over LAN (wired) or wirelessly or over the GSM/cellularmobile networks.

In an embodiment, both video camera and accelerometer/gyro sensor may beused to determine whether there is a seizure. This embodiment mayinclude features and elements of both video and accelerometer/gyrosensor systems, mentioned above. Both detectors may run in parallel.Images from the video system may be used for additional confirmation andvalidation of the data from the accelerometer systems. These systems mayrun 24 hours per day, seven days a week, 365 days per year, all thetime, or on an as-needed basis. The seizure detection system 100,seizure alert system 112, cameras 102 a-n, motion detector 110 may bepowered externally or may be powered by a battery. In an embodiment,there may be multiple levels or thresholds. For example there may be twolevels or thresholds each indicating a different degree of danger orindicating a different degree of certainty that a seizure occurred. Insome cases, there may be a threshold for even suspected conditions maybe recorded. In one embodiment, one threshold or level may indicate thatthere is a problem that is observed, which may record the event orcondition without actually activating an alert and at a second level orthreshold an alert may be activated. Activating the alert may includesending a communication indicating the alert. In an embodiment, alertsare sent for only conditions or seizures that pass certain conditions orlevels. In an embodiment, there may be a button to indicate that thereis a seizure occurring now, which may include an ask-for-help button. Inan embodiment, the patient may be able to manually trigger the alert tobe activated. In an embodiment, if the system detects a problem, but thepatient is fine, the patient can press a button and indicate to ignorethe alarm and that the call was a false alarm. These thresholds can beadjusted by the patient/user. Each user may have different threshold orpersonal requirement on when to record and when to alert. The system mayprovide two adjustable sets of thresholds that can be modified. One setof thresholds is for sending an alert and one set of thresholds is forrecording events that have seizure-like characteristics, but areexpected not to be a seizure, or for events that are near the borderlinebetween being a seizure and not being a seizure. The input parametersmay be entered manually by the user, stored, and reused, so that theuser does not need to input the parameters every time system 100 isturned on or put in use.

In this approach, we utilize the input signal as is. The data can bewindowed (overlapping) over short durations of time and the range ofvalues examined. It is expected that non-seizure-like activities exhibitvalue fluctuations only within a short range of values of acceleration(or gyro sensor), that is, the difference between the minimumacceleration value and the maximum acceleration value over a shortperiod of time is bounded by two relatively close values. On the otherhand, seizure-like activities exhibit a larger fluctuation and thedifference between the minimum acceleration value and the maximumacceleration value.

There at least are four strategies to implement the detection system.Any one of these four strategies is sufficient and can be used to do thedetection (1) learning based and/or (2) rules, conditions and/or logicbased, (3) probabilistic/statistical models and detection methods and(4) analysis of local, regional, global features which include both dataand temporal information. A hybrid of any combination of these fourstrategies can also be used. Seizure detection system 100 may be basedon learning system and incorporate supervised or unsupervised learning,which may include one or more neural-networks, machines that performpattern recognition methods and/or support vector machines, for example.In embodiments including unsupervised learning, positive and negativedata samples are provided to seizure detection system 100 as trainingexamples for classifying patterns of behavior as seizure or non-seizuremotion patterns. After being fed the training examples, seizuredetections system 100 is able to make a determination as to whetherother motion patterns are associated with seizures. By usingunsupervised learning, after a training session and/or after learningfrom experience with actual motion patterns of patient 108, seizuredetection system 100 is able to detect seizures having motion patternsthat do not have features that are otherwise common amongst most otherseizure-like activities. For example, a neural-network or a SupportVector Machine can be trained based on positive and negative datasamples.

Alternatively, the detection system or algorithm can be based on rules,conditions, or equations and pre-defined logic that is patientindependent. For example, the rule or logic can be to compute the localpeaks of motion jerks. If N jerks happen within M seconds, then themotion may be determined to be seizure. For example, if # jerks are >5within 3 secs, then it is a seizure. Typical rules/logic will use one ormore conditions or criteria based on: frequency/number of motion/jerkswithin a time frame, amplitude of the motion, continued change orduration of this motion, 1st or 2nd degree change of these values above.These rules/logic/conditions will change between the type of seizureslike TonicClonic, Partial or

Complex Seizures, etc. Caregivers or patients can also adjust theserules/conditions/thresholds, to better suit their individual needs andtype of seizures. There can also be defined set of types of conditionsor detection rules templates, built in and users can select and chooseand test different ones and pick the ones they like most.

In an embodiment, the motion data from cameras 102 a-n and/or frommotion detector 110 is transformed into the frequency domain, via aFourier transform, or wavelet transform. In the frequency domain, themotion data is analyzed to see whether the motion data includes uniquefeatures in the frequency domain (such as larger coefficientscorresponding to high frequency components) that are expected to befound in motion data from a seizure. Performing a Fourier transform on aset of data taken within a particular window of time may be taken,(which may be referred to as a “windowed Fourier transform” and) whichmay capture the local nature of the signal. Similarly, orthogonalwavelet transforms (such as Daubechies) or another transform of themotion data may be taken (which may also provide a local representationof the signal in terms of the set of basis functions). The transformedmay then be scanned for large coefficients of basis vectors that areexpected to be associated with a seizure. The recognition seizure motionpatterns in the frequency domain may be performed based on a supervisedor unsupervised learning.

In an embodiment there may be a bank of motion detectors, which mayinclude any combination of cameras 102 a-n, motion detector 110, otherhybrid motion detectors, and/or other motion detectors, as describedabove, each running in parallel. The usage of a bank of motion detectorscreates a system that is very robust and that detects seizures with ahigh level of accuracy.

In an embodiment, an accelerometer in a watch measures the accelerationsin the three directions, a_(x), a_(y), and a_(z). Optionally, from theindividual components that magnitude can be computed from a=SQRT(a_(x)²+a_(y) ²+a_(z) ²). Optionally, the magnitude may be used to compute theabsolute first derivative of the acceleration v=|a_(n)−a_(n-1)|,which isequivalent to “jerk.” Optionally, the absolute second derivative may becomputed v′=|a_(n)−2a_(n-1)+a_(n-2)|. The first and/or second derivativeof each component may be computed and/or the first and/or secondderivative of magnitude may be computed. In an embodiment, a count isperformed of all of the peaks of the first and/or second derivative thatoccur during a specified time window that are above a certain threshold.If the number of peaks is greater than a threshold number, it is assumedthat a seizure is occurring.

If a preset number of peaks in the amplitude of the acceleration areobtained within a certain time interval, then it is considered to be aseizure. If less than the preset number of peaks in the amplitude of theacceleration are obtained within the time interval, then it is assumedthat a seizure did not occur. In an embodiment, the variation of eachcomponent of acceleration is analyzed separately (in addition to orinstead of analyzing the magnitude of the acceleration). During aseizure, in each component of the acceleration, the peaks may be morefrequent and shorter than for the amplitude, and consequently, for eachcomponent of acceleration, the threshold for the number of peaks (thatis considered indicative of a seizure) in a given time period may be sethigher and the threshold for the amplitude of the peaks (that isconsidered indicative of a seizure) may be set lower than for themagnitude. The positive and negative examples of motion patterns thatare fed to a neural network or other learning algorithm may include eachcomponent of acceleration and/or the magnitude of the acceleration.

FIG. 1B shows a representation of an embodiment of seizure detectionsystem 150. Seizure detection system 150 band 152, housing 154, display156, and input interface 158. In other embodiments, seizure detectionsystem 150 may include additional components and/or may not include allof the components listed above.

Seizure detection system 150 is an embodiment of a seizure detectionsystem that is a device that is also a wristwatch, that is within adevice that is a wristwatch, or that doubles as a wrist watch. Otherembodiments of seizure detection system 150 may be worn elsewhere on anarm, on a hand, on a leg, on a foot, on a chest, and/or other part of aperson. Seizure detection system 150 may include a motion detector (notshown in FIG. 1B), such as an accelerometer, for motion detector 110(FIG. 1A). Band 152 may be used for fastening seizure detection system150 to a wrist of patient 108 (FIG. 1A). Housing 154 contains thecircuitry for seizure detection system 100 (FIG. 1A). Display 156 maydisplay settings of seizure detection system 150, the time, and/oroutput of seizure detection 156. Input interface 158 may be a series ofbuttons for inputting settings for seizure detection system 150 and/orfor inputting wristwatch settings.

FIG. 2A shows a block diagram of system 200, which may be incorporatedwithin the system of FIG. 1. System 200 may include output system 202,input system 204, memory system 206, processor system 208,communications system 212, and input/output device 214. In otherembodiments, system 200 may include additional components and/or may notinclude all of the components listed above.

System 200 may be an embodiment of seizure detection system 100 in whichseizure detection system 200 is contained within one unit. Alternativelyor additionally, an embodiment of seizure detector 112 may be system200. Output system 202 may include any one of, some of, any combinationof, or all of a monitor system, a handheld display system, a printersystem, a speaker system, a connection or interface system to a soundsystem, an interface system to peripheral devices and/or a connectionand/or interface system to a computer system, intranet, and/or internet,for example. Output system 202 may include lights, such as a red lightand/or a flashing light to indicate a seizure. Output system may includesounds such as beeps, rings, buzzes, sirens, a voice message, and/orother noises. Output system 202 or a part of output system 202 may bekept in the possession of a caretaker or in a location that will catch acaretaker's attention, such as a PDA, cell phone, and/or a monitor of acomputer that is viewed by a caretaker. Output system 202 may send ane-mail, make a phone call, and/or send other forms of messages to alerta concerned party about the occurrence of a seizure.

Input system 204 may include any one of, some of, any combination of, orall of a keyboard system, a mouse system, a track ball system, a trackpad system, buttons on a handheld system, a scanner system, a microphonesystem, a connection to a sound system, and/or a connection and/orinterface system to a computer system, intranet, and/or internet (e.g.,IrDA, USB), for example. Input system 204 may include a motion detectorand/or camera for detecting high frequency motion. Input system 204 or apart of input system 204 may be kept in the possession of a caretaker orin a location easily accessible to a concerned party so that theconcerned party may request current motion information and/or pastmotion and/or seizure information. For example, input system 204 mayinclude an interface for receiving messages from a PDA or cell phone ormay include a PDA and/or cell phone.

Memory system 206 may include, for example, any one of, some of, anycombination of, or all of a long term storage system, such as a harddrive; a short term storage system, such as random access memory; aremovable storage system, such as a floppy drive or a removable drive;and/or flash memory. Memory system 206 may include one or moremachine-readable mediums that may store a variety of different types ofinformation. The term machine-readable medium is used to refer to anymedium capable of carrying information that is readable by a machine.One example of a machine-readable medium is a computer-readable medium.Another example of a machine-readable medium is paper having holes thatare detected that trigger different mechanical, electrical, and/or logicresponses. Memory system 206 may store seizure detection engine and/orinformation about seizures. Memory system 206 will be discussed furtherin conjunction with FIG. 2B. If system 200 is seizure alert system 112,memory system 206 is optional, because the processing and storage ofseizure information may occur elsewhere.

Processor system 208 may include any one of, some of, any combinationof, or all of multiple parallel processors, a single processor, a systemof processors having one or more central processors and/or one or morespecialized processors dedicated to specific tasks. Processor system 208may run a program stored on memory system 206 for detecting seizures,which may be referred to as a seizure detection engine. Processor system208 may implement the algorithm of seizure detection system 200.Processor system 208 may collect the data from one or moreaccelerometers and/or video sensors. Processor system 208 may implementa detection and analysis algorithm on the data. If system 200 is anembodiment of seizure alert system 112, processor system 208 isoptional, because the processor may be located elsewhere.

As a digression, if seizure detection system 112 is not one unit, theprocessor system may be located at one of at least four locations, whichinclude within an external device such as a PC or laptop, within ahandheld device, within a camera, within an accelerometer. Data may bestreamed to the external device via a wired connection (such asLAN/USB/Serial) and/or a wireless connection (such as Wifi/BT). Thehandheld computing device may be a PDA, mobile phone, or other handhelddevice. In other words, the detection engine and algorithm may resideinside the handheld device. The data may be streamed to the mobile phoneor hand-held/PDA, and the processing and/or analysis may be executed onthe handheld device. The processor of seizure detection system 100 maybe located and built into any one of or any combination of cameras 102a-n. In other words, the processor with the detection engine (thesoftware that analyzes the sensor data to determine whether a seizureoccurred) may be embedded inside of any one of or any combination ofcamera 102 a-n and the detection processing may be carried out insidethe camera. In an embodiment, processor system 208 may be located withina handheld device, which may be an embodiment of seizure alert system112 and/or seizure detection system 100 may be a handheld devicestrapped to patient 108 (FIG. 1) in which processor 208 is located.

Communications system 212 communicatively links output system 202, inputsystem 204, memory system 206, processor system 208, and/or input/outputsystem 214 to each other. Communications system 212 may include any oneof, some of, any combination of, or all of electrical cables, fiberoptic cables, and/or means of sending signals through air or water(e.g., wireless communications), or the like. Some examples of means ofsending signals through air and/or water include systems fortransmitting electromagnetic waves such as infrared and/or radio wavesand/or systems for sending sound waves.

Input/output system 214 may include devices that have the dual functionas input and output devices. For example, input/output system 214 mayinclude one or more touch sensitive screens, which display an image andtherefore are an output device and accept input when the screens arepressed by a finger or stylus, for example. The touch sensitive screensmay be sensitive to heat and/or pressure. One or more of theinput/output devices may be sensitive to a voltage or current producedby a stylus, for example. Input/output system 214 is optional, and maybe used in addition to or in place of output system 202 and/or inputdevice 204.

FIG. 2B shows a block diagram of an embodiment of memory system 206.Memory system 206 may include seizure detection algorithm 242,characteristic seizure data 244, records on past seizures 246, anddevice drivers 248. In other embodiments, memory system 206 may includeadditional components and/or may not include all of the componentslisted above.

Seizure detection algorithm 242 analyzes motion data to determinewhether a seizure has occurred. Characteristic seizure data 244 includesinformation characterizing a seizure. Characteristic seizure data 244may include thresholds for various parameters that are indicative of aseizure having taken place. For example, characteristic seizure data mayinclude one or more thresholds for the frequency of oscillation ofvarious body parts during a seizure, thresholds for frequency ofoscillation of the acceleration or other parameter output by theaccelerometer and/or a threshold of the frequency of oscillation ofcantilever that is part of an accelerometer that is included withinmotion detector 110. Characteristic seizure data 244 may includepatterns of data that are indicative of a seizure. Characteristicseizure data 244 may include default data that is not specific to anyone individual and/or may include data that is specific to patient 108.

Records of past seizures 246 may store information about seizures as theseizures are happening, which may be reviewed further at a later date tobetter determine the characteristics of the seizures that are specificto patient 108 so that seizure detection system 100 may more reliablydetect the seizures of patient 108. Additionally or alternatively,records of past seizures 246 may be used for diagnosing and treating theseizure. In an embodiment, all detection results may be recorded on thehard disk of a PC or on an external memory card (SD, Compact Flash,Memstick, etc.). In some instances, knowledge of whether a seizureoccurred may be important to know the effectiveness of a medication orfor other medical reasons. However, some patients are unaware of havingexperienced a seizure. By storing records of past seizures 246, patient108 may nonetheless be informed that a seizure was experienced. The datamay include images, videos, accelerometer, or other motion sensor data.The data may include plots, summaries and/or other forms of data. Thedata may be analyzed and reviewed later by a medical professional fordiagnosis and/or other medical purposes. Device drivers 248 includesoftware for interfacing and/or controlling the motion detector.

Motion Detector With Processor (FIG. 3A)

FIG. 3A shows a block diagram of an embodiment of motion detector 110.Motion detector 110 may include output system 332, input system 334,transmitter/receiver 336, processor system 338, communications system339, memory system 340, which may include seizure detection algorithm342, characteristic seizure data 344, records of past seizures 345,device drivers 346, and/or location determining software 348. Motiondetector 110 may also include location determining hardware 350, motiondetection hardware 352 and clock 354. In other embodiments, motiondetector 110 may include additional components and/or may not includeall of the components listed above.

Output system 332 is optional and may include a display for providingfeedback regarding whether various settings are set and/or may providethe values of the current settings. Input system 334 is optional and mayinclude buttons and/or a pad for entering user settings. Optionally,output system 332 and input system 334 may include an interface forcommunications line 104 to seizure alert system 112.Receiver/transmitter 336 may include an antenna, other hardware, and/orsoftware for communicating wirelessly with other devices, such asseizure alert system 112 (e.g., via receiver 114).

Processor system 338 may be any one of, some of, any combination of, orall of multiple parallel processors, a single processor, a system ofprocessors having one or more central processors and/or one or morespecialized processors dedicated to specific tasks. Processor system 338may run a program stored on memory system for detecting seizures, whichmay be referred to as a seizure detection engine, and/or may performother functions. Communications line 339 may be a bus that allows thevarious components of motion detector 110 to communicate with oneanother.

Memory system 340 may include programs for running motion detector 110and for interfacing with other equipment. Seizure detection algorithm342, characteristic seizure data 344, records of past seizures 345,device drivers 346 have essentially the same description as seizuredetection algorithm 242, characteristic seizure data 244, records onpast seizures 246, and device drivers 248 (FIG. 2B), respectively.Location determining software 348 is optional and includes software fordetermining the location of the patient 108 for situations in whichpatient 108 is having a seizure in an otherwise unknown location. Forexample, location determining software 348 and location determininghardware 350 may be global positioning software and hardware (for a GPSsystem), respectively. Location determining software 348 and locationdetermining hardware 350 are optional and if location determiningsoftware 348 and location determining hardware 350 are globalpositioning software and hardware, location determining hardware 350 mayprocess signals from, and/or communicating with, location determiningsatellites to produce the location determining data that is furtherprocessed by location determining software 348. Motion detectionhardware 352 is the hardware that detects the motion of patient 108(FIG. 1). Motion detection hardware 352 may include an accelerometer,which may include a cantilever with a weight attached to one end and acircuit for detecting deflections of the cantilever.

In an embodiment, the seizure detector may be included within a watch,hand strap, leg strap, and/or strapped to another part of the body. Forexample, an accelerometer/gyro sensor coupled with Bluetooth/ZigBeewireless (or USB or LAN) connectivity may be included in the watch, handstrap, and/or leg strap for detecting seizures. One or more processorsmay be attached to an arm coupled to the accelerometer or other motionsensor and/or incorporated within or attached to a mobile phone. In anembodiment, the watch and the mobile phone in combination may provide acomplete system that records and analyzes data related to possibleseizures. Based on the analysis, there may be a detection of a conditionthat is expected to be a seizure, and an alert and/or other outputcommunication may be sent.

Hand-Worn “Seizure Detection” Watch

In an embodiment, seizure detection system 100 or motion detector 110may be a seizure detection watch, worn by a patient, for example. Theseizure detection watch may contain a wireless transmitter (usingBluetooth/ZigBee/Wifi/RF), an accelerometer or other motion sensor, abattery, and a processor. The sensor detects motion and generatessignals that correspond to the motion. The processor processes thesignals using a detection algorithm that analyzes the signals anddetermines (and thereby detects) whether a seizure occurred. Asdescribed in conjunction with FIG. 1, the Bluetooth transmitter sendsthe seizure detection to the outside world via the Bluetooth, SMS, MMS,WAP, or email, IM, IP messages to another device (which may be a mobilephone, PC, Laptop, or PDA). In an embodiment, the “smart watch” is anintelligent “seizure detector” device that may do some/all the detectionand alerting. In addition, the watch can also have some LED lightsand/or buzzer to indicate the status of detection and what the systemthinks and decides. Hence patients can look at the LEDs or hear thesound and understand the status. There may or may not be any visualdisplays, besides these lights and sounds.

Camera With Processor (FIG. 3B)

FIG. 3B shows a block diagram of camera 360. Camera 360 may includeoutput system 362, input system 364, transmitter/receiver 366, processorsystem 368, communications system 369, memory 380, which may includeseizure detection algorithm 382, characteristic seizure data 384, anddevice drivers 386. Camera 360 may also include image detector 388 andimaging system 390. In other embodiments, camera 360 may includeadditional components and/or may not include all of the componentslisted above.

Output system 362, input system 364, transmitter/receiver 366, processorsystem 368, communications system 369, memory 380, seizure detectionalgorithm 382, characteristic seizure data 384, records on past seizures385, and device drivers 386 have similar descriptions to output system332, input system 334, transmitter/receiver 336, processor system 338,communications system 339, memory system 340, seizure detectionalgorithm 342, characteristic seizure data 344, records on past seizures345, and device drivers 346 of FIG. 3A, respectively. Also, seizuredetection algorithm 382, characteristic seizure data 384, records ofpast seizures 385, device drivers 386 have a similar description asseizure detection algorithm 242, characteristic seizure data 244,records on past seizures 246, and device drivers 248 (FIG. 2B),respectively. However, seizure detection algorithm 382 (FIG. 3B) may betailored for handling optical data, and characteristic seizure data 384and records of past seizures 385 (FIG.

3B) may be optical data, whereas seizure detection algorithm 342 (FIG.3A) may be tailored for accelerometer data and characteristic seizuredata 344 and records of past seizures 345 (FIG. 3A) may be accelerometerdata. Also device drivers 386 (FIG. 3B) may include device drivers forimage detector 388 and/or imaging system 390, whereas device drivers 346(FIG. 3A) may include device drivers for motion detection hardware 352.

Image detector 388 converts optical images into electrical signals thatrepresent the image and/or motion. For example, image detector 388 maybe a charge couple device. Imaging system 390 is the system of lensesand/or other optical components that form the image on image detector388.

Video Seizure Detection Algorithms

The video detection algorithm is one component of the overall system.Video detection can use one or more of at least 3 types of algorithms:

1: Optical Flow based or feature points based

2: Intelligent Motion based and/or Abnormal behavior based motionanalysis

3: Motion vectors (which may be used similar to compression method

Optical Flow Based Detection (FIG. 4)

FIG. 4 is a flowchart of an embodiment of a method 400 of detecting aseizure, based on optical flow. In step 402, feature points that can betracked are determined. optical flow is detected to track the path of acollection of points. Feature points can also be unique or distinguishedpoints on the person's body or cloths. Feature points that can betracked may be corner points and points which exhibit texture in itslocal neighborhood, for example. The optical flow technique works byfirst extracting feature points from the frame that can be trackedreliably. Optical Flow Analysis (or) Feature Point Tracking can bedifferent.

In step 404, the optical flow analysis or feature point trackingalgorithm then tracks the motion or flow of points across successiveframes. The tracking of points may be done in each successive frame oralternatively, some frames can be skipped, depending upon the nature ofmotion.

In step 406, the paths of the tracked points are analyzed and/orplotted. For example, the plot of the points the position as a functionof time, the velocity, and/or acceleration may be extracted. Informationis computed or determined that relate to oscillations of a change ofparameter of motion, such as the frequency of the change, the amplitudeof change, time over which the change occurs (e.g., the period ofoscillation), the area swept out by the body part during the oscillatorymovements, and/or path traced by the points being tracked. The frequencyof, amplitude of, and area swept by the identified points oscillateindicate the frequency of, amplitude of, and area swept out by theoscillation, respectively. The amplitude may indicate the distance thatthe points and/or corresponding objects (e.g., body parts) are moving.Area may indicate the area swept out by a moving object. The path mayindicate the exact nature and movement patterns of the points and/orcorresponding objects. The oscillation analysis as above determineswhether a motion is classified that of a seizure. Time over which aparticular motion occurs may also be indicative of a seizure. There maybe a minimum length of time for the seizure and if the motion does notcontinue over a length of time longer than the minimum length the motionmay be classified as not being part of a seizure.

In step 408 a determination is made based on the oscillation informationwhether a seizure is occurring. Some or all of the above parametersand/or additional parameters may be used to decide if the movement is aseizure. For example, there may be one or more threshold levels for thefrequency, amplitude, and area, and if the motion crosses one or more ofthe thresholds, it may be determined that there is a seizure. Somethresholds may be proportional ratios or functions of these parameters.In step 410, if it is determined that a seizure is occurring, an alertis activated. In an embodiment, method 400 is repeated for each set ofdata until motion detector 110 and/or the processor are turned off.

In an embodiment, each of the steps of method 400 is a distinct step. Inanother embodiment, although depicted as distinct steps in FIG. 4, steps402-410 may not be distinct steps. In other embodiments, method 400 maynot have all of the above steps and/or may have other steps in additionto or instead of those listed above. The steps of method 400 may beperformed in another order. Subsets of the steps listed above as part ofmethod 400 may be used to form their own method.

Intelligent Motion Based and Abnormal Behavior Based a Motion AnalysisAlgorithm (FIG. 5)

FIG. 5 is a flowchart of an embodiment of a method 500 of detecting aseizure, based on motion analysis. In step 502, pairs of a series ofimages are taken (e.g., via a video). In step 504, pairs of consecutiveimages are compared. A video algorithm may analyze a comparison of twoimages to determine whether there is motion, which may be detected basedon pixel changes and image differencing.

In step 506, a determination is made as to whether a seizure hasoccurred based on the length and duration of the body motion.Optionally, information that is not expected to be relevant todetermining whether there was seizure is eliminated using standardtechniques. For example, information about lighting and shadows may beeliminated. In the abnormal motion case, the motion signature, duration,length, and/or area are all used to see the abnormal motion behavior.Seizure patterns can be learned and compared.

In step 508, if it is determined that a seizure is occurring, an alertis activated. Returning to step 506, if it is determined that no seizureoccurs, method 500 terminates. After termination, method 500 may restarton another set of data. In an embodiment, many instances of method 500may be performed concurrently on different sets of data. For example,after a first instance of method 500 starts working on one pair ofimages, a second instance may start working on the next set of data,which may come from the next available pair of images, before the firstinstance of method 500 terminates. In an embodiment, method 500 isrepeated for each set of data until motion detector 110 and/or theprocessor are turned off.

In an embodiment, each of the steps of method 500 is a distinct step. Inanother embodiment, although depicted as distinct steps in FIG. 5, steps502-508 may not be distinct steps. In other embodiments, method 500 maynot have all of the above steps and/or may have other steps in additionto or instead of those listed above. The steps of method 500 may beperformed in another order. Subsets of the steps listed above as part ofmethod 500 may be used to form their own method.

Motion Vectors Based Algorithm (FIG. 6)

FIG. 6 is a flowchart of an embodiment of a method 600 of detecting aseizure, based on patterns of motion vector patterns. In step 602, aseries of images is taken. In step 604, motion vectors are computed.Specifically, two-dimensional vectors that provide offsets from thecoordinates in one picture frame to the coordinates in another pictureframe are computed. The vectors may be created in a manner similar tothe motion vectors created for compression methods or same motionvectors from the compressed video may be used. In an embodiment, themotion vectors are created using IP cameras. In step 606, movementpattern signatures are determined from the motion vectors. In step 608,the movement pattern signatures measured are compared to movementpattern signatures of seizures. A signature may be used for comparisonwith pattern signatures that are determined to fit a signature thatresults from a seizure, and if the signature of the pattern measured isclose enough to (e.g., within a threshold value of the root mean squareof the differences between) the signature of the seizure, an indicationthat a seizure occurred is generated. In step 610, if it is determinedthat a seizure is occurring, an alert is activated.

Returning to step 608, if it is determined that no seizure occurs,method 600 terminates. After termination, method 600 may restart onanother set of data. In an embodiment, many instances of method 600 maybe performed concurrently on different sets of data. For example, aftera first instance of method 600 starts working on one pair of images, asecond instance may start working on the next set of data, which maycome from the next available pair of images, before the first instanceof method 600 terminates. In an embodiment, method 600 is repeated foreach set of data until motion detector 110 and/or the processor areturned off.

In an embodiment, each of the steps of method 600 is a distinct step. Inanother embodiment, although depicted as distinct steps in FIG. 6, steps602-610 may not be distinct steps. In other embodiments, method 600 maynot have all of the above steps and/or may have other steps in additionto or instead of those listed above. The steps of method 600 may beperformed in another order. Subsets of the steps listed above as part ofmethod 600 may be used to form their own method.

Finally, the method of detecting abnormal or specific motion can also bea hybrid algorithm of all 3 video detection methods (described in FIGS.4-6). Or the method of detecting abnormal motion can also be anintegration of/hybrid between Video and Motion/Accelerometer/Sensoralgorithms joined and fused together as final one.

Accelerometer Seizure Detection Algorithm (FIG. 7)

FIG. 7 is a flowchart of an embodiment of a method 700 of detectingseizures by measuring motion, via an accelerometer (and/or gyro sensor).An accelerometer sensor provides the acceleration and orientation of thebody. The accelerometer is secured to the patient's hand, arm, legs,and/or any other part of body that shakes and has the seizure movements.The following steps are used to detect the seizure from theaccelerometer data.

In step 702, the sensor data is obtained by a time based sampling of themotion.

For example, 1000 or 10,000 samples per second are collected. In anembodiment, the samples may be an amount of deflection of a cantilever.The data sampling may be in any one or all X, Y, and Z axes. The dataper axis may be one-dimensional numerical accelerometer data. The motiondata may be sampled over time. Hence for the X and Y axes, there will betwo data streams. For triple axes data from axes X, Y, and Z, there willbe 3 data streams. In an embodiment, the amplitude and frequency aremeasured as part of the time based data stream. The amplitude,frequency, change of position, and acceleration may be measured from theaccelerometer or other motion sensor data. The data may be tracked overtime. The data can be tracked in every time interval or specified timeintervals may be skipped. The time intervals at which the data issampled define the sampling frequency.

In optional step 704, the path of the data is analyzed or plotted. Datasuch as points, position, acceleration, velocity, and/or speed areextracted. If the points, positions, velocities, and acceleration werealready determined as part of step 702, step 704 may be skipped.

In step 706, an oscillation analysis is performed, as described above,which may be used to determine whether a seizure takes place. Thefrequency, amplitude, time, and/or path of the oscillation may bedetermined. Frequency may determine how frequently the sensor andobjects oscillate. The amplitude may indicate the amount of distancethat the objects move. The path may indicate the exact nature andmovement patterns of the objects. The frequency, amplitude, and/or pathmay be used to analyze oscillations and decide if the movement is aseizure.

Returning to step 708, if it is determined that no seizure occurs,method 700 terminates. After termination, method 700 may restart onanother set of data. In an embodiment, many instances of method 700 maybe performed concurrently on different sets of data. For example, aftera first instance of method 700 starts working on one pair of images, asecond instance may start working on the next set of data, which maycome from the next available pair of images, before the first instanceof method 700 terminates. In an embodiment, method 700 is repeated foreach set of data until motion detector 110 and/or the processor areturned off.

The detection algorithm can use any of one or more of the followingmathematical methods. In one embodiment, the peak and amplitude of theoscillation are checked, and compared to thresholds value of the peaksand amplitude. In an embodiment, if the peak and/or amplitude aregreater than the threshold, then a determination is made that theoscillation is associated with a seizure. An absolute and/or relativethreshold may be used to find abnormalities, which may indicate aseizure.

In another embodiment, a search is made for repeated peaks and valleysin the one dimensional sensor data (for X, Y, and/or Z). This technologyuses the motion vector patterns based algorithm. Repeated anddistinguished peaks and valleys may indicate seizures.

In another embodiment, a search is made for duplicate peaks on otheraxes. In other words, one axis may have stronger peaks while the othersmay have weaker peaks. In some cases all axes can be stronger or weaker.However, neighboring axes can provide a valuable confirmation when thepeaks on one axis have corresponding peaks on another axis.

In another embodiment, software and/or hardware neural networks or otherlearning methods may determine abnormal patterns to detect seizures. Inanother embodiment, exact template patterns or signal patterns can beused to match other signal patterns. A prior known seizure pattern canbe used to compare the signal pattern with the known template and if thepattern detected matches the known seizure pattern within a giventolerance, then it is expected that a seizure occurred. These neuralnetworks or other machines using other learning methods, can be eithersupervised or unsupervised learning.

In another embodiment, the position may be analyzed by determining thefirst derivative and the second derivative of a signal that isindicative of the position as a function of time. The first and secondderivative of the position signal may be monitored to determine whetherthe first and second derivative are within a range that is considered tobe an average and/or normal change of position (an average and normalfirst derivative dx/dt and an average and normal rate of change ofposition, which is the 2nd derivative, d²x/dt²). When the first and/orsecond derivative are abnormal, or beyond a threshold, then anindication is generated that a seizure may have been detected.Additionally, if the periodic changes of the first and second derivativeare outside of a certain range, it may be an indication that a seizurehas occurred. Periodic changes in the second derivate are the thirdderivates, which are the impulses, which may be used to characterizejerky motion. Similarly, if the third derivative (or another derivative)is beyond a threshold or is not within a range that is considerednormal, an indication that a seizure occurred may be generated.Additionally, if the pattern of times at which the third derivativesrise above a certain threshold matches that of a known pattern for aseizure and/or occur at a frequency that is expected to be indicative ofa seizure, an indication that a seizure has occurred is generated.

In another embodiment, statistical learning or probabilistic methods areused. Machine learning strategies based on Bayesian Network (Bayes net)and HMM (Hidden Markov Models) and other statistical learning orprobabilistic methods can be used for detection of seizure.

In another embodiment, local, regional, and/or global features aredetected. The features may be a collection of data taken from aneighborhood of a signal with temporal information. Local features arecharacteristics of signal and/or data in a small region of the data.Local features are a function of time (e.g., the features of a plot ofthe signal as a function of time), regional features covers more time,and global features are an average or a collection of local/regionalfeatures over longer time. Both local and global features can be acombination of both shape and time/temporal based. The local and globalfeatures are detected and compared to known local, regional and globalfeatures that are expected to characterize seizure features to determinewhether a seizure has occurred. If the local, regional and globalfeatures that are associated with a seizure occur, then a signal isgenerated indicating that seizure has occurred.

Individual Person's Seizure Signature

In one mode or embodiment, a person's seizure data or motion signaturemay be measured as a seizure occurs. “Seizure detection” parameters(frequency, amplitude, patterns) can be customized as a “seizuresignature” for each patient. This signature can be adjusted andconfigured for each patient if required for higher accuracy, as opposedto standard factory defaults.

Instead of a fixed seizure signature, the person's signature may bedetermined over time. Then the detection algorithm will adapt andfine-tune the seizure detection parameters based on the individual'ssignature patterns-such as frequency, amplitude, time, area, path,and/or other parameters).

FIG. 8 shows a graph 800 of three orthogonal components of accelerationof an arm.

Graph 800 includes a vertical axis 802 a-c, horizontal axis 804 a-c, andplots 806, 808, and 810. Horizontal axis 802 a-c is the time axis, andvertical axes 804 a-c are the amplitude axes. Plots 806, 808, and 810are plots of each of the three components of acceleration labeled X, Y,and Z, which are measured in a reference frame that is stationary withrespect to the wrist.

Graph 800 shows 9 peaks with about 5 seconds. The threshold for thenumber of peaks within a window of 6 seconds should be 9 peaks or less.The magnitude for the peaks of the y component of acceleration is 6cm/sec², and the threshold for a single component of acceleration shouldbe less than 6 cm/sec² .

FIG. 9 shows a graph 900 of two parameters of motion. Graph 900 includesa vertical axis 902, horizontal axis 904, and plots 906, 908, and 910.Horizontal axis 902 is the time axis, and vertical axes 904 a-cis theamplitude axis. Plot 906 plots the magnitude of the acceleration vector.Plot 908 plots the first derivative of the magnitude of acceleration.Plot 910 is also a plot of the first derivative of the magnitude ofacceleration. However, peaks that were below a predetermined thresholdwere removed. If the number of peaks within a specified window of timeare greater than a predetermined number, it is an indication of aseizure.

In FIGS. 8 and 9 the units for acceleration are m/sec² and the units forimpulse or jerk are m/sec³. In an embodiment, for at least some patientsthe threshold for the magnitude of acceleration may be about 7 m/sec².The threshold for a “jerk,” or impulse, may be about a number less than4 m/sec³, e.g., 3 m/sec³, 3.5 m/sec³, or 3.8 m/sec³. The threshold forthe frequency of peaks in acceleration may be 3/(3 seconds) (e.g., 1Hz). In a window of 3 seconds there should be at least 3 peaks. Thethreshold for the number of peaks in the impulse should be at least 3,and within a window of about 3 seconds there should be at least 3 peaksin the impulse/jerk. In other embodiments, the units and the values forthe thresholds above may be proportional to those given above.

FIG. 10 shows an embodiment of a seizure detection, analyzing, andmonitoring device.

Extensions and Alternatives

Each embodiment disclosed herein may be used or otherwise combined withany of the other embodiments disclosed. Any element of any embodimentmay be used in any embodiment.

Although the invention has been described with reference to specificembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the true spirit and scope of theinvention. In addition, modifications may be made without departing fromthe essential teachings of the invention.

The present invention is not to be limited in scope by the specificembodiments described herein, which are intended as single illustrationsof individual aspects of the invention, and functionally equivalentmethods and components are within the scope of the invention. Indeed,various modifications of the invention, in addition to those shown anddescribed herein, will become apparent to those skilled in the art fromthe foregoing description. Such modifications are intended to fallwithin the scope of the appended claims. All publications, includingpatent documents and scientific articles, referred to in thisapplication and the bibliography and attachments are incorporated byreference in their entirety for all purposes to the same extent as ifeach individual publication were individually incorporated by reference.The article “a” as used herein means one or more unless indicatedotherwise. All headings are for the convenience of the reader and shouldnot be used to limit the meaning of the text that follows the heading,unless so specified.

What is claimed is:
 1. A method to detect a seizure, comprising:electronically collecting motion data produced by a sensor physicallyassociated with a person; determining from the collected motion data viaa processor communicatively coupled to the sensor at least one valuecharacterizing the motion data; comparing the at least one valuecharacterizing the motion data to at least one corresponding valuecharacterizing abnormal motion; determining whether the seizure hasoccurred based on the comparing; and activating a seizure alert signalwhen the determined seizure has occurred.
 2. The method of claim 1,wherein the at least one corresponding value characterizing abnormalmotion includes a motion pattern model characterizing a seizure, andwherein the determining includes determining whether the motion datamatches the motion pattern model characterizing the seizure within apredetermined tolerance.
 3. The method of claim 1, further comprising:retrieving historic motion data previously stored in a memory; andapplying the historic motion data to generate the at least onecorresponding value characterizing abnormal motion.
 4. The method ofclaim 1, wherein the motion data is video data, the method furthercomprising: identifying at least one distinguishable feature in thevideo data; determining locations of the distinguishable feature acrossmultiple picture frames of the video data; and determining a motion pathfor the distinguishable feature, the motion path for the distinguishablefeature being the at least one value characterizing the motion, whereinthe comparing includes comparing the motion path for the distinguishablefeature to a motion path characterizing the seizure, and wherein thedetermining whether a seizure has occurred includes determining whetherthe motion path for the distinguishable feature matches the motion pathcharacterizing the seizure within a predetermined tolerance.
 5. Themethod of claim 1, wherein the motion data represents oscillatorymotion.
 6. The method of claim 5, wherein the at least one valuecharacterizing the motion data represents a frequency of oscillation,and wherein the at least one corresponding value characterizing abnormalmotion represents a predetermined threshold, and wherein determiningwhether the seizure has occurred is based on the frequency ofoscillation being higher than the predetermined threshold.
 7. The methodof claim 6, the motion data is derived from an optical flow or featurepoint analysis.
 8. The method of claim 6, wherein the motion data isderived from a plurality of motion vectors.
 9. A method to detectabnormal motion in a person, comprising: producing first sensor data ata first time, the first sensor data representing a first physical stateof a portion of a body of a person, the first sensor data produced by atleast one of an accelerometer, a gyro sensor, and a camera; producingsecond sensor data at a second time, the second time after the firsttime, the second sensor data representing a second physical state of theportion of the body of the person, the second sensor data produced bythe at least one of the accelerometer, the gyro sensor, and the camera;mathematically associating the first sensor data with the second sensordata to obtain a motion value; comparing the motion value to an abnormalmotion threshold; and activating an abnormal motion signal based on thecomparing.
 10. The method of claim 9, wherein the first sensor data andthe second sensor data are digital values representing at least one ofamplitude, frequency, and acceleration.
 11. The method of claim 9,further comprising: repeating, a plurality of times, the acts ofproducing first and second sensor data, the act of mathematicallyassociating the first and second sensor data, and the act of comparing;tracking at least one point of the portion of the body of the person;and deriving oscillation information as the motion value.
 12. The methodof claim 9, further comprising: repeating, a plurality of times, theacts of producing first and second sensor data, the act ofmathematically associating the first and second sensor data, and the actof comparing; computing at least one motion vector; generating asignature representing the at least one motion vector, the signaturebeing the motion value.
 13. The method of claim 9, further comprising:physically attaching the at least one of the accelerometer, the gyrosensor, and the camera to either the person or furniture the person isin contact with.
 14. The method of claim 9, wherein the abnormal motionis indicative of a motion disorder, and wherein the motion disorder isone of epilepsy, ataxia, dystonia, dyskinesia, Parkinson's disease,chorea, tremor, tics, myoclonus, and restless leg syndrome.
 15. A systemfor detecting a seizure or abnormal motion, said system comprising: aninput means for inputting motion parameters, said input means configuredfor inputting a template; a seizure detection engine; and a processorconfigured for activating an alert, wherein said processor is configuredto be part of said seizure detection engine or separate from saidseizure detection engine or both.
 16. The system of claim 15, furthercomprising at least one of a location determination hardware and aglobal positioning software for determining a location of a user,further comprising at least one of a camera for video capture, amicrophone for audio capture, and a sensor for motion capture.
 17. Thesystem of claim 15, wherein the template comprises threshold valuescomprising one or more of a value relating to at least one of afrequency of oscillations of a body part, oscillatory motion, frequencyof oscillations, frequency of motion within a time frame, amplitude ofthe motion, duration of the motion, seizure intensity or abnormal motionintensity based on amplitude, seizure intensity or abnormal motionintensity based on frequency, acceleration magnitude peaks in a timeframe, a first derivative of a magnitude of acceleration, and a secondderivative of a magnitude of acceleration.
 18. The system of claim 15,wherein the seizure detection engine is configured for matching a motionpattern to the template, wherein the motion pattern is captured by oneor more of an accelerometer, a gyroscopic sensor, a video-capturecamera, and an audio-capture microphone, wherein said motion pattern isrepresented as a sum of basis functions multiplied by coefficients anddetermining if a magnitude of one or more coefficients crosses apredetermined threshold as an indication that at least one of a seizureor abnormal motion has occurred, wherein the matching the motion patternis performed at least in part in a device that is remote from otherfunctionality of the system, and wherein the template comprises aseizure data or abnormal motion threshold for at least one motionparameter, and past seizure data or abnormal motion data for a user ofthe system.
 19. The system of claim 15, further comprising at least oneof a statistical model, a neural network, or a training routine forlearning behaviors associated with a specific type of motion fordetecting at least one of a seizure or abnormal motion.
 20. The systemof claim 15, further comprising at least one or more of an option for auser to manually trigger an alert to a concerned party, an option for arequirement of a user confirmation prior to an alert being sent to aconcerned party, an option for cancelling an alert to a concerned party,and an option to snooze an alert, and further comprising a transmittercapable of sending the alert to a remote location.